Evolution of the semiconductor manufacturing industry is placing ever greater demands on yield management and, in particular, on metrology and inspection systems. Critical dimensions are shrinking while wafer size is increasing. Economics is driving the industry to decrease the time for achieving high-yield, high-value production. Thus, minimizing the total time from detecting a yield problem to fixing it determines the return-on-investment for the semiconductor manufacturer.
Repeater defects are a concern to semiconductor manufacturers. Repeater defects are defects that appear on a wafer with some regular periodicity and that show some fixed relationship to the die layout on a reticle or stepping pattern on a wafer. Reticle defects are a common cause of repeater defects. Reticle defects that can cause repeater defects include, for example, extra chrome pattern on a mask plate, missing chrome on a mask plate, particulates on the mask plate or on the reticle, and damage to the pellicle.
For example, a print check application can include detection of reticle defects. These reticle defects occur on multiple reticles across the wafer and can be seen as either soft repeaters (less than 50% occurrence) or hard repeaters (more than 50% occurrence). A print check application can identify all unique repeater candidates and identify all locations where the repeater defect is printed.
Previously, reticle repeater inspection was performed by die-to-die or reticle-to-reticle inspection where each die or reticle is compared with surrounding dies or reticles. Repeater identification was performed after the inspection, such as during post processing. This technique has reduced sensitivity because of higher surrounding noise. These kinds of inspections also generate a high count of events during the scan. Thus, repeater events with signal may fail to be detected because of tool capabilities to handle high defect counts.
Broad band plasma (BBP) tools may use die-to-die inspection for repeater identification. However, this technique has lower sensitivity due to higher surrounding noise. Furthermore, the BBP tools may be lack the capability to handle a necessary number of defects before post-processing, such as greater than 1.7 million defects.
Reticle repeater inspection also was performed by robust average algorithms such as Repeater-in Array (RIA) or Standard Reference Die 1 (SRD 1) based on a robust average image. Such algorithms remove an outlier event for every pixel. This reduces random events during detection. This technique may not accurately identify printability variations or perform a robust recipe setup. In such algorithms, after a repeater is detected, the locations are copied to all the inspected dies or reticles on the row. However, separating the defect from a printed location to a not printed location can be challenging. The only available attribute to do so (e.g., repeater signal) is not always effective. Such attributes may be ineffective when analyzed across different repeaters.
BBP tools also can be used with robust average algorithms to detect repeaters. After a robust average algorithm detects a repeater, the location is copied to all the inspected reticles on the die row. Using a robust average instead can suffer from recipe setup and printability variation challenges. Separating the defect from a printed location to a not printed location can be challenging because it uses the repeater signal.
Furthermore, high nuisance rates are a common problem during these inspections. This can prevent the BBP tool from running hot enough to find small defects (e.g., into the noise floor). Previous techniques calculated attributes of individual defects, but separating defects of interest (DOI) and nuisance defects is often difficult, which can result in a high nuisance rate.
Therefore, a new technique to detect repeater defects is needed.